title: Understanding the need of ventricular pressure for the estimation of diastolic biomarkers.
abstract: The diastolic function (i.e., blood filling) of the left ventricle (LV) is determined by its capacity for relaxation, or the decay in residual active tension (AT) generated during systole, and its constitutive material properties, or myocardial stiffness. The clinical determination of these two factors (diastolic residual AT and stiffness) is thus essential for assessing LV diastolic function. To quantify these two factors, in our previous work, a novel model-based parameter estimation approach was proposed and successfully applied to multiple cases using clinically acquired motion and invasively measured ventricular pressure data. However, the need to invasively acquire LV pressure limits the wide application of this approach. In this study, we address this issue by analyzing the feasibility of using two kinds of non-invasively available pressure measurements for the purpose of inverse mechanical parameter estimation. The prescription of pressure based on a generic pressure-volume (P-V) relationship reported in literature is first evaluated in a set of 18 clinical cases (10 healthy and 8 diseased), finding reasonable results for stiffness but not for residual active tension. We then investigate the use of non-invasive pressure measures, now available through imaging techniques and limited by unknown or biased offset values. Specifically, three sets of physiologically realistic synthetic data with three levels of diastolic residual active tension (i.e., impaired relaxation capability) are designed to quantify the percentage error in the parameter estimation against the possible pressure offsets within the physiological limits. Maximum errors are quantified as 11 % for the magnitude of stiffness and 22 % for AT, with averaged 0.17 kPa error in pressure measurement offset using the state-of-the-art non-invasive pressure estimation method. The main cause for these errors is the limited temporal resolution of clinical imaging data currently available. These results demonstrate the potential feasibility of the estimation diastolic biomarkers with non-invasive assessment of pressure through medical imaging data.
authors.affiliation: Department of Computer Science, Oxford University, Oxford, UK, jiahe.xi@cs.ox.ac.uk.
authors.forename: Jiahe
authors.initials: J
authors.lastname: Xi
authors.forename: Wenzhe
authors.initials: W
authors.lastname: Shi
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.forename: Reza
authors.initials: R
authors.lastname: Razavi
authors.forename: Nicolas P
authors.initials: NP
authors.lastname: Smith
authors.forename: Pablo
authors.initials: P
authors.lastname: Lamata
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism*; D003198:Computer Simulation; D003971:Diastole; D005260:Female; D006321:Heart / Q000503:physiopathology*; D006352:Heart Ventricles / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D009200:Myocardial Contraction / Q000502:physiology; D009206:Myocardium / Q000473:pathology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D013314:Stress, Mechanical; D013599:Systole; D016277:Ventricular Function, Left; D017725:Ventricular Pressure / Q000502:physiology*; D055815:Young Adult
chemical_list: D015415:Biomarkers